





"""simple docstring"""





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
       from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
       SCREAMING_SNAKE_CASE__   =  get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
       from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

SCREAMING_SNAKE_CASE__   =  128_022
SCREAMING_SNAKE_CASE__   =  128_028

@require_sentencepiece
class 						lowercase   (   _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = MaMaaaTokenizer
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = True





      def        _snake_case     (			self					)						->	Optional[Any]:
            super().setUp()

            lowerCAmelCase    		=			["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
            lowerCAmelCase    		=			dict(zip(lowercase ,		range(len(lowercase					)					)					)					)
            lowerCAmelCase    		=			Path(self.tmpdirname					)
            save_json(lowercase ,		save_dir / VOCAB_FILES_NAMES["""vocab_file"""]					)
            if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                  copyfile(lowercase ,		save_dir / VOCAB_FILES_NAMES["""spm_file"""]					)

            lowerCAmelCase    		=			MaMaaaTokenizer.from_pretrained(self.tmpdirname					)
            tokenizer.save_pretrained(self.tmpdirname					)





      def        _snake_case     (			self ,		**lowercase					)						->	str:
            return MaMaaaTokenizer.from_pretrained(self.tmpdirname ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            return (
                "This is a test",
                "This is a test",
            )





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			"""</s>"""
            lowerCAmelCase    		=			0

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase					) ,		lowercase					)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase					) ,		lowercase					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			self.get_tokenizer()
            lowerCAmelCase    		=			list(tokenizer.get_vocab().keys()					)

            self.assertEqual(vocab_keys[0] ,		"""</s>"""					)
            self.assertEqual(vocab_keys[1] ,		"""<unk>"""					)
            self.assertEqual(vocab_keys[-1] ,		"""<s>"""					)
            self.assertEqual(len(lowercase					) ,		tokenizer.vocab_size + len(tokenizer.get_added_vocab()					)					)





      @unittest.skip("""Skip this test while all models are still to be uploaded."""					)
      def        _snake_case     (			self					)						->	Dict:
            pass





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			self.get_tokenizer()

            lowerCAmelCase    		=			tokenizer.tokenize("""This is a test"""					)
            self.assertListEqual(lowercase ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]					)

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(lowercase					) ,		[2, 3, 4, 5, 6] ,		)

            lowerCAmelCase    		=			tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]					)
            self.assertListEqual(lowercase ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]					)

            lowerCAmelCase    		=			tokenizer.convert_tokens_to_string(lowercase					)
            self.assertEqual(lowercase ,		"""This is a test"""					)





      @slow
      def        _snake_case     (			self					)						->	Tuple:
            # fmt: off
            lowerCAmelCase    		=			{"""input_ids""": [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=lowercase ,		model_name="""facebook/m2m100_418M""" ,		revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e""" ,		)







@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowercase   (   unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = 'facebook/m2m100_418M'
      _SCREAMING_SNAKE_CASE							      = [
          'In my opinion, there are two levels of response from the French government.',
          'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
      ]
      _SCREAMING_SNAKE_CASE							      = [
          'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
          'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
      ]

      # fmt: off
      _SCREAMING_SNAKE_CASE							      = [EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]





      @classmethod
      def        _snake_case     (			cls					)						->	List[Any]:
            lowerCAmelCase    		=			MaMaaaTokenizer.from_pretrained(
                cls.checkpoint_name ,		src_lang="""en""" ,		tgt_lang="""fr"""					)
            lowerCAmelCase    		=			1
            return cls





      def        _snake_case     (			self					)						->	Union[str, Any]:
            self.assertEqual(self.tokenizer.get_lang_id("""ar"""					) ,		128_006					)
            self.assertEqual(self.tokenizer.get_lang_id("""en"""					) ,		128_022					)
            self.assertEqual(self.tokenizer.get_lang_id("""ro"""					) ,		128_076					)
            self.assertEqual(self.tokenizer.get_lang_id("""mr"""					) ,		128_063					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			self.tokenizer.get_vocab()
            self.assertEqual(len(lowercase					) ,		self.tokenizer.vocab_size					)
            self.assertEqual(vocab["""<unk>"""] ,		3					)
            self.assertIn(self.tokenizer.get_lang_token("""en"""					) ,		lowercase					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			"""en"""
            lowerCAmelCase    		=			self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
            self.assertListEqual(self.expected_src_tokens ,		lowercase					)





      def        _snake_case     (			self					)						->	Dict:
            self.assertIn(lowercase ,		self.tokenizer.all_special_ids					)
            # fmt: off
            lowerCAmelCase    		=			[FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
            # fmt: on
            lowerCAmelCase    		=			self.tokenizer.decode(lowercase ,		skip_special_tokens=lowercase					)
            lowerCAmelCase    		=			self.tokenizer.decode(generated_ids[1:] ,		skip_special_tokens=lowercase					)
            self.assertEqual(lowercase ,		lowercase					)
            self.assertNotIn(self.tokenizer.eos_token ,		lowercase					)





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase    		=			tempfile.mkdtemp()
            lowerCAmelCase    		=			self.tokenizer.lang_token_to_id
            self.tokenizer.save_pretrained(lowercase					)
            lowerCAmelCase    		=			MaMaaaTokenizer.from_pretrained(lowercase					)
            self.assertDictEqual(new_tok.lang_token_to_id ,		lowercase					)





      @require_torch
      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase    		=			"""en"""
            lowerCAmelCase    		=			"""fr"""

            lowerCAmelCase    		=			self.tokenizer(self.src_text ,		text_target=self.tgt_text ,		padding=lowercase ,		return_tensors="""pt"""					)

            lowerCAmelCase    		=			shift_tokens_right(
                batch["""labels"""] ,		self.tokenizer.pad_token_id ,		self.tokenizer.eos_token_id					)

            for k in batch:
                  lowerCAmelCase    		=			batch[k].tolist()
            # batch = {k: v.tolist() for k,v in batch.items()}
            # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
            # batch.decoder_inputs_ids[0][0] ==
            assert batch.input_ids[1][0] == EN_CODE
            assert batch.input_ids[1][-1] == 2
            assert batch.labels[1][0] == FR_CODE
            assert batch.labels[1][-1] == 2
            assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]





      @require_torch
      def        _snake_case     (			self					)						->	str:
            lowerCAmelCase    		=			"""mr"""
            self.assertListEqual(self.tokenizer.prefix_tokens ,		[self.tokenizer.get_lang_id("""mr"""					)]					)
            self.assertListEqual(self.tokenizer.suffix_tokens ,		[self.tokenizer.eos_token_id]					)

            lowerCAmelCase    		=			"""zh"""
            self.assertListEqual(self.tokenizer.prefix_tokens ,		[self.tokenizer.get_lang_id("""zh"""					)]					)
            self.assertListEqual(self.tokenizer.suffix_tokens ,		[self.tokenizer.eos_token_id]					)





      @require_torch
      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""mr"""
            self.tokenizer._switch_to_target_mode()
            self.assertListEqual(self.tokenizer.prefix_tokens ,		[self.tokenizer.get_lang_id("""mr"""					)]					)
            self.assertListEqual(self.tokenizer.suffix_tokens ,		[self.tokenizer.eos_token_id]					)
            self.tokenizer._switch_to_input_mode()
            self.assertListEqual(self.tokenizer.prefix_tokens ,		[self.tokenizer.get_lang_id(self.tokenizer.src_lang					)]					)

            lowerCAmelCase    		=			"""zh"""
            self.tokenizer._switch_to_target_mode()
            self.assertListEqual(self.tokenizer.prefix_tokens ,		[self.tokenizer.get_lang_id("""zh"""					)]					)
            self.assertListEqual(self.tokenizer.suffix_tokens ,		[self.tokenizer.eos_token_id]					)
            self.tokenizer._switch_to_input_mode()
            self.assertListEqual(self.tokenizer.prefix_tokens ,		[self.tokenizer.get_lang_id(self.tokenizer.src_lang					)]					)





      @require_torch
      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			self.tokenizer._build_translation_inputs("""A test""" ,		return_tensors="""pt""" ,		src_lang="""en""" ,		tgt_lang="""ar"""					)

            self.assertEqual(
                nested_simplify(lowercase					) ,		{
                    # en_XX, A, test, EOS
                    """input_ids""": [[128_022, 58, 4_183, 2]],
                    """attention_mask""": [[1, 1, 1, 1]],
                    # ar_AR
                    """forced_bos_token_id""": 128_006,
                } ,		)













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






